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How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

Posted in robotics/AI

Francesco Cagnetta, Leonardo Petrini, Umberto M. Tomasini, Alessandro Favero, and Matthieu Wyart Institute of Physics EPFL, Institute of Electrical Eng.


A hierarchical model of high-dimensional data reveals how deep neural networks leverage their multiple layers to reduce the data dimensionality and learn from a finite set of examples.